Preprint Highlight: Self-supervised deep learning encodes high-resolution features of protein subcellular localization.

Abstract

The ability to quantitatively characterize protein subcellular spatial organization is key for understanding how cells orchestrate their many functions. This study presents cytoself, a deep‐learning self‐supervised method to compute lower dimensional representations of protein spatial localization distributions by forcing different images of the same protein to have similar representations. Cytoself was applied to the public resource OpenCell to build a protein localization atlas that was validated by identifying known protein complexes that cluster together. The authors demonstrate how to interpret cytoself's output by identifying specific features in the localization representation associated with specific localization patterns in the images and by reverse engineering the neural network. Cytoself could serve as a “discovery tool” to generate hypotheses regarding unknown protein functions and protein‐protein interactions.